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Metals 2017, 7(4), 147; doi:10.3390/met7040147

Kohonen Neural Network Classification for Failure Process of Metallic Organic Coating in Corrosion Environment

School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
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Author to whom correspondence should be addressed.
Academic Editor: Jae Myung Lee
Received: 21 December 2016 / Revised: 29 March 2017 / Accepted: 14 April 2017 / Published: 20 April 2017
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Abstract

A deeper insight into the changing states of corrosion during certain exposure circumstances has been investigated by applying Kohonen networks. The Kohonen network has been trained by four sets of samples and tested using another sample. All the sample data were collected during accelerated corrosion experiments and the network took the changing rate of impedance of each cycle as an input. Compared with traditional classification, the Kohonen artificial network method classifies corrosion process into five sub-processes which is a refinement of three typical corrosion processes. The two newly defined sub-processes of corrosion—namely, pre-middle stage and post-middle stage—were introduced. The EIS data and macro-morphology for both sub-processes were analyzed through accelerated experiments. The classification results of the Kohonen artificial network are highly consistent with the predictions based on impedance magnitude at low frequency, which illustrates that the Kohonen network classification is an effective method for predicting the failure cycles of polymer coatings. View Full-Text
Keywords: coating; corrosion; EIS; Kohonen neural network; classification coating; corrosion; EIS; Kohonen neural network; classification
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MDPI and ACS Style

Xu, Y.; Ran, J.; Chen, H. Kohonen Neural Network Classification for Failure Process of Metallic Organic Coating in Corrosion Environment. Metals 2017, 7, 147.

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